A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer

Sarah Moeller, Ghazaleh Kazeminejad, Andrew Cowell, Mans Hulden


Abstract
We experiment with training an encoder-decoder neural model for mimicking the behavior of an existing hand-written finite-state morphological grammar for Arapaho verbs, a polysynthetic language with a highly complex verbal inflection system. After adjusting for ambiguous parses, we find that the system is able to generalize to unseen forms with accuracies of 98.68% (unambiguous verbs) and 92.90% (all verbs).
Anthology ID:
W18-4802
Volume:
Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editor:
Judith L. Klavans
Venue:
PYLO
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12–20
Language:
URL:
https://aclanthology.org/W18-4802
DOI:
Bibkey:
Cite (ACL):
Sarah Moeller, Ghazaleh Kazeminejad, Andrew Cowell, and Mans Hulden. 2018. A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer. In Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages, pages 12–20, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer (Moeller et al., PYLO 2018)
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PDF:
https://aclanthology.org/W18-4802.pdf